Xiangyu Ge

TL;DR: In this paper, the authors used the multi-spectral instrument (MSI) onboard the Sentinel-2 onboard ship for the monitoring and mapping of soil salinity in arid and semi-arid areas.

TL;DR: It is concluded that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy via UAV hyperspectral imagery on a regional scale and might improve management and conservation strategies for agroecosystem systems in arid regions.

TL;DR: Combining RS data sets and their derived TCW within a Cubist framework yielded accurate regional salinity map, and MSI image with finer spatial resolution performed better than OLI.

TL;DR: In this paper, partial least square support vector machine (PLS-SVM) models were calibrated using spectral parameters selected based on a single dimension (FOD), two-dimensional index (NDI), and three-dimensional indices (MNDI) and subsequently applied to estimate SOMC.

TL;DR: The spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework, which offers a new perspective and a basic scientific support for water quality monitoring in arid regions.